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Apply data governance to maximise data insights

How IT can apply data governance to help the business trust analytics insights and gain the most value from big data

As more data – big, small, internal or external – is freed from its functional silos and made accessible to non-specialist audiences, and the use of advanced analytics becomes more pervasive, companies have an even greater opportunity to derive deeper insights about their business and customers.

In fact, businesses are already realising benefits from big data. A survey by Accenture Analytics found 92% of organisations report satisfaction with their big data, specifically in finding sources of revenue and winning customers. Also, a growing number of companies, particularly larger ones, see big data as extremely important to their organisation.

However, decision-makers can still be sceptical of the insights being presented to them. 

There are a variety of possible reasons for this, including how decision-makers might reject insights if they don’t fully understand the analytics tools and complex methodologies behind the outputs and visualisations. 

They may also dismiss insights if they find it counter-intuitive or don’t trust the data supporting the analysis.

How to help the business trust its data

Companies can help business stakeholders trust analytics insights and gain the most value from big data by taking the following approach to data governance.

A key role for the chief data officer (CDO) is to establish the enterprise’s data as a trusted, strategic asset. In this role, the CDO helps an enterprise manage the availability, usability, integrity and security of its data

The following are elements of an effective data governance strategy organisations can employ to empower decision-makers to get the most out of their big data projects.

1. Make data a business asset

Put a coherent strategy behind the organisation's data initiatives rather than pursuing point solutions tied to data quality or a data governance process. A key question that should be answered when developing this strategy is: How do you make data an enterprise asset to drive informed decision-making?

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2. Training

Conduct widespread data and analytics training for all levels of the firm’s management. Management doesn’t necessarily need a detailed understanding of big data, advanced analytics or big data technologies, but they should be able to grasp the fundamentals of these concepts so they can ask the right questions of their data analysis teams.

3. Definitions

Install common definitions for the use of data throughout the organisation – a common language is critical for understanding insights and establishing consistent conclusions. This is especially important for companies that operate across multiple countries.

4. Access

Understand which departments and individuals should have appropriate access to the enterprise's data. As more data is available, it shouldn’t be assumed that everyone should have equal access to that data. Some people or groups may only need access to high level data while others may require more granular data so they can conduct their own analysis. Effective security and compliance depends on getting this right.

5. Storage

Approach how data can be stored with a new mindset so the data infrastructure is fit for purpose. Organisations should explore different storage platforms that can support the variety of organisational data needs.

Prepare for a data-driven future

Some financial data, for example, will have usage characteristics that require it be stored on very secure and robust platforms that allow data to be 100% accurate, available 24/7, archived and have strict access protocols. It should also be a data system that meets auditor and regulator standards.

A marketing department, by contrast, might have different needs, such as analysing social media data, much of it unstructured, from Twitter and blog feeds to uncover consumer perceptions of the company's products. This data might be less constrained by regulatory and compliance constraints and may be required to support real-time interactions at a significant scale without ever going down. In this type of environment, tools in the Hadoop ecosystem, such as Spark, Hbase and Cassandra, could be used.

The enterprise may also want to consider the use of a data sandbox, which is a scalable and exploratory platform used to mine an organisation's rich information sets, leveraging machine learning and visualisation technologies. This system can enable a trusted source of data to allow experimentation and sharing without risk to the organisation's core data systems.

As businesses are confronted with new and ever-expanding data assets, there will be a need to adopt new and unfamiliar technologies. We see benefits will accrue over a period of time and the pace of adoption will accelerate across the enterprise as it gains experience with new tools and techniques and establishes trusted, curated sources of data. 

The next generation of data governance strategy can endow organisations with a better understanding and trust of big data and its insights. When decision-makers are confident in their data and the ability of their platforms to deliver, they will fully embrace data-driven decision-making to establish a competitive differentiation over their slower, less data-driven rivals.

About the authors

Ray Eitel-Porter is managing director of Accenture Analytics, leading the UK & Ireland analytics practice. 



Nick Millman is managing director of Accenture Digital, leading big data and analytics delivery across Europe, Africa and Latin America.

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